






"""simple docstring"""







from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class    lowerCamelCase__   (			_UpperCAmelCase					):
    SCREAMING_SNAKE_CASE						=     42
    SCREAMING_SNAKE_CASE						=     42






    def __init__(					self      ,A      ,A   ):
          super().__init__()
          self.register_modules(unet=lowerCAmelCase__      ,scheduler=lowerCAmelCase__   )






    @torch.no_grad()
    def __call__(					self      ,A = 1      ,A = 2_000      ,A = None      ,A = "pil"      ,A = True      ,**A      ,):
          UpperCAmelCase							     =			self.unet.config.sample_size
          UpperCAmelCase							     =			(batch_size, 3, img_size, img_size)

          UpperCAmelCase							     =			self.unet

          UpperCAmelCase							     =			randn_tensor(lowerCAmelCase__      ,generator=lowerCAmelCase__   ) * self.scheduler.init_noise_sigma
          UpperCAmelCase							     =			sample.to(self.device   )

          self.scheduler.set_timesteps(lowerCAmelCase__   )
          self.scheduler.set_sigmas(lowerCAmelCase__   )

          for i, t in enumerate(self.progress_bar(self.scheduler.timesteps   )   ):
                UpperCAmelCase							     =			self.scheduler.sigmas[i] * torch.ones(shape[0]      ,device=self.device   )

                # correction step
                for _ in range(self.scheduler.config.correct_steps   ):
                      UpperCAmelCase							     =			self.unet(lowerCAmelCase__      ,lowerCAmelCase__   ).sample
                      UpperCAmelCase							     =			self.scheduler.step_correct(lowerCAmelCase__      ,lowerCAmelCase__      ,generator=lowerCAmelCase__   ).prev_sample

                # prediction step
                UpperCAmelCase							     =			model(lowerCAmelCase__      ,lowerCAmelCase__   ).sample
                UpperCAmelCase							     =			self.scheduler.step_pred(lowerCAmelCase__      ,lowerCAmelCase__      ,lowerCAmelCase__      ,generator=lowerCAmelCase__   )

                UpperCAmelCase     ,   UpperCAmelCase							     =			output.prev_sample, output.prev_sample_mean

          UpperCAmelCase							     =			sample_mean.clamp(0      ,1   )
          UpperCAmelCase							     =			sample.cpu().permute(0      ,2      ,3      ,1   ).numpy()
          if output_type == "pil":
                UpperCAmelCase							     =			self.numpy_to_pil(lowerCAmelCase__   )

          if not return_dict:
                return (sample,)

          return ImagePipelineOutput(images=lowerCAmelCase__   )




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a    :	List[Any]         =    logging.get_logger(__name__)

__a    :	List[Any]         =    {
    """MIT/ast-finetuned-audioset-10-10-0.4593""": (
        """https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"""
    ),
}




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					Optional[Any]     					=		'''audio-spectrogram-transformer'''





			def __init__(  self ,  lowerCAmelCase__=7_68 ,  lowerCAmelCase__=12 ,  lowerCAmelCase__=12 ,  lowerCAmelCase__=30_72 ,  lowerCAmelCase__="gelu" ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=0.02 ,  lowerCAmelCase__=1E-12 ,  lowerCAmelCase__=16 ,  lowerCAmelCase__=True ,  lowerCAmelCase__=10 ,  lowerCAmelCase__=10 ,  lowerCAmelCase__=10_24 ,  lowerCAmelCase__=1_28 ,  **lowerCAmelCase__ ,  )	->   Optional[Any]:

							'''simple docstring'''


							super().__init__(**lowerCAmelCase__		)

							__lowercase					     =				hidden_size
							__lowercase					     =				num_hidden_layers
							__lowercase					     =				num_attention_heads
							__lowercase					     =				intermediate_size
							__lowercase					     =				hidden_act
							__lowercase					     =				hidden_dropout_prob
							__lowercase					     =				attention_probs_dropout_prob
							__lowercase					     =				initializer_range
							__lowercase					     =				layer_norm_eps
							__lowercase					     =				patch_size
							__lowercase					     =				qkv_bias
							__lowercase					     =				frequency_stride
							__lowercase					     =				time_stride
							__lowercase					     =				max_length
							__lowercase					     =				num_mel_bins